Artificial intelligence and corporate carbon neutrality: A qualitative exploration

Many firms have established formal carbon neutrality (CN) targets in response to the increasing climate risk and related regulatory requirements. Subsequently, they have implemented various measures and adopted multiple approaches to attain these goals. Academic research has given due attention to firms' efforts in this direction. However, past studies have primarily focused on non-digital and process-oriented approaches to achieving CN, with the potential of digital technologies such as artificial intelligence (AI) remaining less explored. Our study aims to address this gap by qualitatively examining the use of AI for pursuing CN, drawing insights from firms with prior experience in the area. We analyzed the collected qualitative data to identify four key dimensions that capture different nuances of applying AI for achieving CN: (a) implementing AI for direct and indirect control of emissions, (b) accepting the strategic trade-offs related to funding, data and systems concerns, and social priorities, (c) overcoming organizational and human-related impediments, and (d) acknowledging the significant impact of AI in terms of gains in business model efficiency and measurable CN target attainment, which ultimately contribute to CN. Based on our findings, we propose a convergence-divergence model encompassing the positive aspects, inhibiting factors, synergies, and offsets necessary for firms to leverage AI to achieve net-zero emissions effectively. Overall, our study contributes to the discourse on the utilization of AI for CN in a comprehensive manner.